A Robust Optical Flow Tracking Method Based on Prediction Model for Visual-Inertial Odometry

Indirect method is widely used in the field of visual SLAM at present, and it can be divided into feature matching method and optical flow tracking method according to whether matching descriptors are needed in the tracking process. Compared to feature matching method, optical flow does not need to spend too much time on calculating and matching feature points, but it is easy to be affected by illumination changes and fast motion. In recent years, the visual-inertial fusion method improves the localization accuracy and robustness on the condition that the illumination is poor or image blur caused by fast motion. A robust optical flow tracking method based on prediction model for visual-inertial odometry is proposed in this paper. A new optical flow and brightness correction method based on a linear illumination model is used to deal with the large changes of illumination. We also proposed an initial optical flow prediction method with the pre-integration of IMU and reprojection model between image frames to make the optical flow more robust. The results of optical flow are also used to judge the image quality of specific areas to make sure the extracted feature points are more stable and suitable for tracking. Finally, the method is proved that can outperform other state-of-the-art VIO methods in experiments on the public benchmark even without loop closure.